U.S. patent application number 11/703248 was filed with the patent office on 2007-08-23 for method for noise reduction in tomographic image data records.
Invention is credited to Anja Borsdorf, Rainer Raupach.
Application Number | 20070196008 11/703248 |
Document ID | / |
Family ID | 38282273 |
Filed Date | 2007-08-23 |
United States Patent
Application |
20070196008 |
Kind Code |
A1 |
Borsdorf; Anja ; et
al. |
August 23, 2007 |
Method for noise reduction in tomographic image data records
Abstract
A method is disclosed for noise reduction in 3D volume data
records from tomographic recordings. In at least one embodiment,
the method includes generating at least two statistically
independent equally dimensioned 3D volume data records for the same
location and situation. In at least one embodiment of the method,
the at least two statistically independent 3D volume data records
are respectively subjected to 3D wavelet transformation with low
pass filtering and high pass filtering in the three spatial
directions of the three dimensional volume data record, and a
respective initial data record with wavelet coefficients is
calculated. Further, correlation coefficients for identical wavelet
coefficients are ascertained from the initial data records and a
new wavelet data record is calculated by weighting the wavelet
coefficients from at least one initial data record on the basis of
the ascertained correlation coefficients for the wavelet
coefficients from the initial data records. Finally, a new 3D
volume data record is transformed back from the new wavelet data
record.
Inventors: |
Borsdorf; Anja; (Hochstadt,
DE) ; Raupach; Rainer; (Adelsdorf, DE) |
Correspondence
Address: |
HARNESS, DICKEY & PIERCE, P.L.C.
P.O.BOX 8910
RESTON
VA
20195
US
|
Family ID: |
38282273 |
Appl. No.: |
11/703248 |
Filed: |
February 7, 2007 |
Current U.S.
Class: |
382/131 |
Current CPC
Class: |
G06T 2207/10072
20130101; G06T 2207/20016 20130101; G06T 2207/30004 20130101; G06T
2200/04 20130101; G06T 11/005 20130101; G06T 5/10 20130101; G06T
2207/20064 20130101; G06T 5/002 20130101 |
Class at
Publication: |
382/131 |
International
Class: |
G06K 9/00 20060101
G06K009/00 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 8, 2006 |
DE |
10 2006 005 804.6 |
Claims
1. A method for noise reduction in 3D volume data records from
tomographic recordings, comprising: generating at least two
statistically independent equally dimensioned 3D volume data
records for the same location and situation; respectively
subjecting the at least two statistically independent 3D volume
data records to 3D wavelet transformation with low pass filtering
and high pass filtering in the three spatial directions of the
three dimensional volume data record, and calculating a respective
initial data record with wavelet coefficients; ascertaining
correlation coefficients for identical wavelet coefficients from
the initial data records; calculating a new wavelet data record by
weighting the wavelet coefficients from at least one initial data
record on the basis of the ascertained correlation coefficients for
the wavelet coefficients from the initial data records; and
transforming a new 3D volume data record back from the new wavelet
data record.
2. The method as claimed in claim 1, wherein the wavelet data
records contain a first group of wavelet coefficients, calculated
exclusively by low pass filtering in the three spatial
directions.
3. The method as claimed in claim 1, wherein the wavelet data
records contain a second group of wavelet coefficients, calculated
by two low pass filtering operations in two of the three spatial
directions and one high pass filtering operation in the respective
remaining third spatial direction.
4. The method as claimed in claim 1, wherein the wavelet data
records contain a third group of wavelet coefficients calculated by
two high pass filtering operations in two of the three spatial
directions and one low pass filtering operation in the respective
remaining third spatial direction.
5. The method as claimed in claim 1, wherein the wavelet data
records contain a fourth group of wavelet coefficients calculated
exclusively by high pass filtering in the three spatial
directions.
6. The method as claimed in claim 2, wherein at least one of the
same correlation function and the same rating criterion is used for
all four groups of wavelet coefficients.
7. The method as claimed in claim 2, wherein at least one of
different correlation functions and different rating criteria are
used for at least one of the three groups of wavelet coefficients
which have been produced by at least one high pass filtering
operation.
8. The method as claimed in claim 2, wherein the weighting of the
wavelet coefficients for the purpose of calculating the new wavelet
data record is made the same within all three groups of wavelet
coefficients which have been produced by at least one high pass
filtering operation.
9. The method as claimed in claim 2, wherein the weighting of the
wavelet coefficients for the purpose of calculating the new wavelet
data record is made different for at least two groups of wavelet
coefficients which have been produced by at least one high pass
filtering operation.
10. The method as claimed in claim 1, wherein the new wavelet data
record is calculated from precisely one of the at least two initial
data records.
11. The method as claimed in claim 1, wherein the new wavelet data
record is calculated from a combination of the at least two initial
data records.
12. The method as claimed in claim 1, wherein the correlation
function used at least for the second group of wavelet coefficients
is a cross correlation function.
13. The method as claimed in claim 12, wherein the cross
correlation function used for the second group of wavelet
coefficients is the following function: g j = G A j x .times. G B j
x + G A j y .times. G B j y + G A j z .times. G B j z ( G A j x ) 2
+ ( G A j y ) 2 + ( G A j z ) 2 .times. ( G B j x ) 2 + ( G B j y )
2 + ( G B j z ) 2 , ##EQU9## where the indexes A and B relate to
the at least two statistically independent 3D volume data records A
and B, and the index j is the calculation level in the wavelet
transformation.
14. The method as claimed in claim 1, wherein the correlation
function used at least for the third group of wavelet coefficients
is a cross correlation function.
15. The method as claimed in claim 14, wherein the cross
correlation function used for the third group of wavelet
coefficients is the following function: f i = F A j yz .times. F B
j yz + F A j xz .times. F B j xz + F A j xy .times. F B j xy ( F A
j yz ) 2 + ( F A j xz ) 2 + ( F A j xy ) 2 .times. ( F B j yz ) 2 +
( F B j xz ) 2 + ( F B j xy ) 2 , ##EQU10## where the indexes A and
B relate to the at least two statistically independent 3D volume
data records A and B, and the index j is the calculation level in
the wavelet transformation.
16. The method as claimed in claim 1, wherein the correlation
function used at least for the fourth group of wavelet coefficients
is a cross correlation function.
17. The method as claimed in claim 16, wherein the cross
correlation function used for the fourth group of wavelet
coefficients (D) is the following function: d j = 1 2 + ( D A j
.times. D B j ( D A j ) 2 + ( D B j ) 2 ) P .di-elect cons. [ 0 , 1
] ##EQU11## where the indexes A and B relate to the at least two
statistically independent 3D volume data records A and B, the index
j is the calculation level. in the wavelet transformation, and the
exponent P is usable as a variable for setting the degree of
selection.
18. The method as claimed in claim 1, wherein a Haar wavelet is
used for the 3D wavelet transformation.
19. A method, comprising: applying the method of claim 1 in X-ray
computer tomography, using at least two statistically independent
volume data records, each comprising a multiplicity of voxels.
20. A method, comprising: applying the method of claim 1 in X-ray
computer tomography, using at least two statistically independent
data records, each comprising a multiplicity of sectional image
data records, and the 3D wavelet transformation being carried out
across sectional images.
21. A method, comprising: applying the method of claim 1 to volume
data records from Nuclear Magnetic Resonance tomography.
22. A method, comprising: applying the method of claim 1 to volume
data records in Positron Emission Tomography.
23. A method, comprising: applying the method of claim 1 to volume
data records in ultrasound tomography.
24. A storage medium, at least one of integrated into a processor
and for a processor in a tomography system, including at least one
computer program or program modules stored thereon which, upon
execution on the processor in a tomography system, executes the
method as claimed in claim 1.
25. A tomography system including a processor, at least one
computer program or program modules being stored thereon which,
upon execution on the processor in a tomography system, executes
the method as claimed in claim 1.
26. The method as claimed in claim 2, wherein the wavelet data
records contain a second group of wavelet coefficients, calculated
by two low pass filtering operations in two of the three spatial
directions and one high pass filtering operation in the respective
remaining third spatial direction.
27. The method as claimed in claim 26, wherein the wavelet data
records contain a third group of wavelet coefficients calculated by
two high pass filtering operations in two of the three spatial
directions and one low pass filtering operation in the respective
remaining third spatial direction.
28. The method as claimed in claim 27, wherein the wavelet data
records contain a fourth group of wavelet coefficients calculated
exclusively by high pass filtering in the three spatial directions.
Description
PRIORITY STATEMENT
[0001] The present application hereby claims priority under 35
U.S.C. .sctn.119 on German patent application number DE 10 2006 005
804.6 filed Feb. 8, 2006, the entire contents of which is hereby
incorporated herein by reference.
[0002] 1. Field
[0003] Embodiments of the invention generally relates to a method
for noise reduction in tomographic image data records, for example
through wavelet breakdown of two statistically independent data
records, determination of the correlations between these data
records and reconstruction of a new volume data record from
weighted data.
[0004] 2. Background
[0005] Laid-open specification DE 103 05 221 Al discloses methods
for noise reduction, these involving two statistically independent,
identical or spatially similar 2D sectional images or projections
determining wavelet coefficients in the image plane, and the
ascertained cross correlations between the wavelet coefficients
being taken as a basis for using the latter, following appropriate
weighting, to calculate a new image with rejection of uncorrelated
components. Although such image editing rejects a large proportion
of the noise, better distinction between noise which is actually
present and small image structures would be desirable.
SUMMARY
[0006] In at least one embodiment of the invention, an improved
method is disclosed for noise reduction in tomographic image data
records through wavelet breakdown.
[0007] The inventors have recognized, in at least one embodiment,
that the reliability of the rating of correlations between the
wavelet coefficients is critically dependent on the signal-to-noise
ratio, which is in turn determined by the statistics of the pixels
used to calculate the wavelet coefficients. In two dimensions, this
involves the use of (L.sub.w).sup.2 pixels in each level, where
L.sub.w is the length of the one-dimensional filters associated
with a wavelet.
[0008] In the case of short wavelets, for example Haar wavelets,
the analysis is accordingly based only on very few pixels, namely
four in the case of the Haar base. There is therefore the risk that
the noise will have a relatively high likelihood of being
interpreted as a real structure and will therefore be retained in
the freshly reformatted image. This firstly reduces the maximum
possible noise reduction, and secondly, with heavy weighting of the
coefficients, incorrectly retained noise emerges clearly and
reduces the impression of quality for the filtered image
material.
[0009] The inventors, in at least one embodiment, therefore propose
not only performing the wavelet breakdown in a plane of an image
data record but rather extending it to the entire measured volume
with all three spatial directions. This is particularly simple and
effective in the case of modern CT systems, which reconstruct 3D
volume data records showing an almost isotropic resolution in all
three spatial directions. It is therefore possible to use the
statistics not only in a plane corresponding to two spatial
directions but rather in three spatial directions which are
independent of one another. The closer the resolution of the 3D
volume data record under consideration in the third dimension used
to the resolution in a sectional plane which is at right angles
thereto, that is to say the more isotropic the resolution, the
better and statistically more significant use can be made of the
information in this third dimension.
[0010] In the case of CT image data records, the third dimension
corresponds to the z direction or system axis direction. This
increases the number of pixels used for the correlation calculation
to (L.sub.w).sup.3, and a distinction between genuine and random
correlations is improved by the factor L.sub.w.
[0011] Three-dimensional wavelet breakdown includes the following
coefficients, which can be classified into four groups. When
classifying into groups, the division criterion used is the number
of one-dimensional high pass filtering operations or low pass
filtering operations when ascertaining the respective wavelet.
[0012] 1st group, called "low pass component": TP.sub.x{circle
around (X)}TP.sub.y{circle around (X)}TP.sub.z.fwdarw.T 2nd group,
called one-dimensional "directional derivations": HP.sub.x{circle
around (X)}TP.sub.y{circle around
(X)}TP.sub.z.fwdarw.G.sup.x,TP.sub.x{circle around
(X)}HP.sub.y{circle around
(X)}TP.sub.z.fwdarw.G.sup.y,TP.sub.x{circle around
(X)}TP.sub.y{circle around (X)}HP.sub.z.fwdarw.G.sup.z 3rd group,
called "surface diagonal components": TP.sub.x{circle around
(X)}HP.sub.y{circle around
(X)}HP.sub.z.fwdarw.F.sup.yz,HP.sub.x{circle around
(X)}TP.sub.y{circle around
(X)}HP.sub.z.fwdarw.F.sup.xz,HP.sub.x{circle around
(X)}HP.sub.y{circle around (X)}TP.sub.z.fwdarw.F.sup.xy 4th group,
called "space diagonal component": HP.sub.x{circle around
(X)}HP.sub.y{circle around (X)}HP.sub.z.fwdarw.D.
[0013] In this context, TP and HP are the one-dimensional low and
high pass filters associated with the wavelet transformation, the
indexes of these filters respectively representing the filter
direction for high pass filtering. This produces the wavelet
coefficients T, G.sup.x, G.sup.y, G.sup.z, F.sup.yz, F.sup.xz,
F.sup.xy and D.
[0014] The three differential components from the 2nd to 4th groups
contain the information about edges and noise in the frequency band
of the respective level of the wavelet calculation. The correction
analysis can be performed particularly advantageously on a separate
basis in the various components and is then carried out for the
purpose of weighting the wavelet coefficients involved.
[0015] The 1st order terms, that is to say the directional
derivations G.sup.x, G.sup.y and G.sup.z, can be used to calculate
the following normalized cross correlation function in the level j,
by way of example, g j = G A j x .times. G B j x + G A j y .times.
G B j y + G A j z .times. G B j z ( G A j x ) 2 + ( G A j y ) 2 + (
G A j z ) 2 .times. ( G B j x ) 2 + ( G B j y ) 2 + ( G B j z ) 2 .
##EQU1##
[0016] On the basis of gj, the wavelet coefficients G.sub.. . .
,j.sup.x, G.sub.. . . ,j.sup.y, G.sub.. . . ,j.sup.y can then be
weighted for the purpose of noise reduction. In the simplest case,
this can be done on the basis of threshold value. That is to say
that all wavelet coefficients G.sub.. . . ,j with
g.sub.j<C.sub.g are set to zero and are consequently no longer
included in the back transformation (wavelet synthesis). A
particular advantage is the direct use of g.sub.j or a power of
g.sub.j as a weight for the contributions by the wavelet
coefficients G.sub.. . . ,j.sup.x, G.sub.. . . ,j.sup.y, G.sub.. .
. ,j.sup.y.
[0017] The 2nd order components, that is to say the surface
diagonal components F.sup.yz, F.sup.xz and F.sup.xy, can be treated
in a similar manner to the wavelet coefficients G.sub.. . . ,j,
that is to say that the magnitude f i = F A j yz .times. F B j yz +
F A j xz .times. F B j xz + F A j xy .times. F B j xy ( F A j yz )
2 + ( F A j xz ) 2 + ( F A j xy ) 2 .times. ( F B j yz ) 2 + ( F B
j xz ) 2 + ( F B j xy ) 2 ##EQU2## is used to rate the correlations
and to weight the coefficients F.sub.. . . ,j.
[0018] By way of example, the diagonal term can be used with the
following cross correlation function: d j = 1 2 + ( D A j .times.
.times. D B j ( D A j ) 2 + ( D B j ) 2 ) P .di-elect cons. [ 0 , 1
] , ##EQU3## where the exponent P can be used as a variable for
setting the degree of selection.
[0019] In one advantageous practical implementation of at least one
embodiment, the method described above can be carried out in real
time. To this end, the data need to be subjected to high pass and
low pass filtering online during setup of the tomographic volume
data. Since, in the case of a CT, the volume data are reconstructed
in line with the scanning progress along the z axis or system axis,
and 3D wavelet transformation also requires data situated in the
scanning direction, a certain advance needs to occur between the
scan and the wavelet transformation, so that the 3D wavelet
transformation trails the scan and the reconstruction by a few
layers. One possible procedure for this is described in connection
with FIG. 2 which follows.
[0020] In line with previously outlined basis idea of the inventors
in at least one embodiment, they propose a method for noise
reduction in 3D volume data records from tomographic recordings,
which has at least the following method steps: [0021] at least two
statistically independent equally dimensioned 3D volume data
records (A, B) for the same location and situation are generated,
[0022] the at least two statistically independent 3D volume data
records (A, B) are respectively subjected to 3D wavelet
transformation with low pass filtering and high pass filtering in
the three spatial directions of the three dimensional volume data
record, and a respective initial data record with wavelet
coefficients is calculated, [0023] correlation coefficients for
identical wavelet coefficients are ascertained from the initial
data records, [0024] a new wavelet data record is calculated by
weighting the wavelet coefficients from at least one initial data
record on the basis of the ascertained correlation coefficients for
the wavelet coefficients from the initial data records, [0025]
finally, a new 3D volume data record is transformed back from the
wavelet data record or the new wavelet data records.
[0026] This method, in at least one embodiment, makes additional
information available over the prior art in a further dimension in
order to make a correlation decision, and this decision becomes
accordingly safer. With regard to different options for obtaining
statistically independent volume data records, reference is made by
way of example to the previously unpublished German patent
application with the file reference DE 10 2005 012 654.5, the
entire contents of which are hereby incorporated herein by
reference.
[0027] Advantageously, the wavelet data records may be grouped such
that a first group of wavelet coefficients is obtained which are
calculated exclusively by low pass filtering (TP) in the three
spatial directions (x,y,z), so that the following is true:
TP.sub.x{circle around (X)}TP.sub.y{circle around
(X)}TP.sub.z.fwdarw.T. In addition, it is pointed out that this
group of wavelet coefficients T always acts as an intermediate
image and is broken down further in the next computation level.
Hence, only the components of the wavelet coefficients which
contain at least one high pass filtering operation are weighted in
each computation plane j.
[0028] The wavelet data records may also contain a second group of
wavelet coefficients which are calculated by two low pass filtering
operations (TP) in two of the three spatial directions (x,y,z) and
one high pass filtering operation (HP) in the respective remaining
third spatial direction (x,y,z), so that the following is true:
HP.sub.x{circle around (X)}TP.sub.y{circle around
(X)}TP.sub.z.fwdarw.G.sup.x, TP.sub.x{circle around
(X)}HP.sub.y{circle around (X)}TP.sub.z.fwdarw.G.sup.y,
TP.sub.x{circle around (X)}TP.sub.y{circle around
(X)}HP.sub.z.fwdarw.G.sup.z.
[0029] Furthermore, the wavelet data records may contain a third
group of wavelet coefficients which are calculated by two high pass
filtering operations (HP) in two of the three spatial directions
(x,y,z) and one low pass filtering operation (TP) in the respective
remaining third spatial direction (x,y,z), so that the following is
true: TP.sub.x{circle around (X)}HP.sub.y{circle around
(X)}HP.sub.z.fwdarw.F.sup.yz, HP.sub.x{circle around
(X)}TP.sub.y{circle around (X)}HP.sub.z.fwdarw.F.sup.xz,
HP.sub.x{circle around (X)}HP.sub.y{circle around
(X)}TP.sub.z.fwdarw.F.sup.xy.
[0030] Finally, the wavelet data records may contain a fourth group
of wavelet coefficients which are calculated exclusively by high
pass filtering (HP) in the three spatial directions (x,y,z), so
that the following is true: HP.sub.x{circle around
(X)}HP.sub.y{circle around (X)}HP.sub.z.fwdarw.D.
[0031] Firstly, the same correlation function and/or the same
rating criterion may be used as a simplification for all groups of
wavelet coefficients, for example the three groups of wavelet
coefficients G.sup.x, G.sup.y, G.sup.z; F.sup.yz, F.sup.xz,
F.sup.xyand D.
[0032] A more flexible variant and one which is easier to match to
the respective circumstances is when different correlation
functions and/or different rating criteria are used for at least
one of the three groups of wavelet coefficients G.sup.x, G.sup.y,
G.sup.z; F.sup.yz, F.sup.xz, F.sup.xy and D. In particular, the
rating of the two groups of wavelet coefficients G.sup.x, G.sup.y,
G.sup.zand F.sup.yz, F.sup.xz, F.sup.xy may turn out to be
different than for the group of wavelet coefficients D.
[0033] It is also a simple matter to make the weighting of the
wavelet coefficients for the purpose of calculating the, new
wavelet data record the same within all four groups of wavelet
coefficients T; G.sup.x, G.sup.y, G.sup.z; F.sup.yz, F.sup.xz,
F.sup.xy and D.
[0034] More advantageous is a flexible variant in which the
weighting of the wavelet coefficients for the purpose of
calculating the new wavelet data record is made different for at
least two groups of wavelet coefficients T; G.sup.x, G.sup.y,
G.sup.z; F.sup.yz, F.sup.xz, F.sup.xy and D.
[0035] In addition, the new wavelet data record can be calculated
from precisely one of the at least two initial data records or from
a combination of the at least two initial data records.
[0036] In one particular variant of at least one embodiment of the
inventive method, the correlation function used at least for the
second group of wavelet coefficients (G.sup.x, G.sup.y, G.sup.z)
may be a cross correlation function. In this case, the following
function is suitable for the second group of wavelet coefficients
(G.sup.x, G.sup.y, G.sup.z), for example: g j = G A j x .times. G B
j x + G A j y .times. G B j y + G A j z .times. G B j z ( G A j x )
2 + ( G A j y ) 2 + ( G A j z ) 2 .times. ( G B j x ) 2 + ( G B j y
) 2 + ( G B j z ) 2 , ##EQU4## where the indexes A and B relate to
the at least two statistically independent 3D volume data records A
and B, and the index j is the calculation level in the wavelet
transformation.
[0037] Accordingly, the correlation function used at least for the
third group of wavelet coefficients (F.sup.yz, F.sup.xz, F.sup.xy)
may be a cross correlation function. In this case, the following
function is suitable, for example: f i = F A j yz .times. F B j yz
+ F A j xz .times. F B j xz + F A j xy .times. F B j xy ( F A j yz
) 2 + ( F A j xz ) 2 + ( F A j xy ) 2 .times. ( F B j yz ) 2 + ( F
B j xz ) 2 + ( F B j xy ) 2 , ##EQU5## where, in this case too, the
indexes A and B relate to the at least two statistically
independent 3D volume data records A and B, and the index j is the
calculation level in the wavelet transformation.
[0038] Finally, the correlation function used at least for the
fourth group of wavelet coefficients (D) may be a cross correlation
function, where particularly the following function: d .times. j =
1 2 + ( D A j .times. .times. D B j .times. ( D A j ) 2 + ( D B j )
2 ) P .di-elect cons. [ 0 , 1 ] ##EQU6## is suitable. In this case
too, the indexes A and B relate to the at least two statistically
independent 3D volume data records A and B, the index j is the
calculation level in the wavelet transformation, and the exponent P
may be used as a variable for setting the degree of selection.
[0039] As an example of statistically independent volume data
records, mention may be made of those which have been reconstructed
from even projection values on the one hand or uneven projection
values on the other hand. Also, statistically independent volume
data records may come from different focus/detector combinations
with an angular offset. Another possibility may also be, by way of
example, to combine the projections of different spring focus
positions in a spring focus system to form respective statistically
independent projections and to calculate respective statistically
independent volume data records therefrom.
[0040] On account of its simple design, a Haar wavelet is
particularly suitable for online processing for 3D wavelet
transformation. However, it should be pointed out that other
transformations are also possible. For example, spline or Daubechy
wavelets may be used.
[0041] The method described above, in at least one embodiment, may
preferably be applied for X-ray computer tomography, with at least
two statistically independent volume data records A and B, each
comprising a multiplicity of voxels, being used.
[0042] Alternatively, the method, in at least one embodiment, may
be applied in X-ray computer tomography, with at least two
statistically independent data records A and B, each comprising a
multiplicity of sectional image data records, being used and the 3D
wavelet transformation being carried out across sectional
images.
[0043] With regard to the use of embodiments of the inventive
method in CT, it should be pointed out that said method may firstly
be used to improve the image quality with a constant applied
radiation dose or to reduce the radiation dose while maintaining
the image quality. The same applies to application in positron
emission tomography (PET) or other tomographic methods using
ionizing radiation.
[0044] Furthermore, it is also within the realm of at least one
embodiment of the invention for improving image quality to transfer
the noise rejection method described above to volume data records
from NMR tomography (NMR=Nuclear Magnetic Resonance) or ultrasound
tomography.
[0045] At least one embodiment of the invention also includes a
storage medium which is integrated in a processor in a tomography
system or which is intended for a processor in a tomography system
and which has at least one computer program or program modules
which, upon execution on the processor in a tomography system,
execute(s) the methods outlined above during operation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0046] The text below gives a more detailed description of the
invention using the specific example embodiment of CT imaging with
reference to FIGS. 1 to 4, where only the features which are
required in order to understand the invention are shown. For these,
the following reference symbols have been used: 1: CT system; 2:
first X-ray tube; 3: first multirow detector; 4: second X-ray tube;
5: second multirow detector; 6: gantry housing; 7: patient; 8:
patient's couch; 9: system axis; 10: processor and control unit;
11: internal memory; 12: volume data records; 13.1, 13.2:
statistically independent volume data records; 14.1, 14.2: wavelet
transformation; 15: noise rejection; 16: correlation-dependent
weighting of the wavelet coefficients; 17: new volume data records;
18: inventive method; Prg,-Prg,: computer programs; A, B:
statistically independent volume data records; j: computation
levels; imax: maximum number of computation levels; L.sub.w: length
of the one-dimensional filters; P: projection; PI, PI':
statistically independent subprojections; S: radiation data record;
S', S'': statistically independent radiation data records; S.sub.1
to S.sub.j: rays from a projection; S.sub.1 to S.sub.k: rays from
the first volume element; .alpha..sub.1 to .alpha..sub.n:
projection angles.
[0047] Specifically:
[0048] FIG. 1 shows a CT system with a schematic method
illustration;
[0049] FIG. 2 shows a basic outline of a wavelet
transformation;
[0050] FIG. 3 shows splitting of a parallel projection into two
complete subordinate parallel projections;
[0051] FIG. 4 shows splitting of a voxel scan in line with the
inventive method.
DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS
[0052] It will be understood that if an element or layer is
referred to as being "on", "against", "connected to", or "coupled
to" another element or layer, then it can be directly on, against,
connected or coupled to the other element or layer, or intervening
elements or layers may be present. In contrast, if an element is
referred to as being "directly on", "directly connected to", or
"directly coupled to" another element or layer, then there are no
intervening elements or layers present. Like numbers refer to like
elements throughout. As used herein, the term "and/or" includes any
and all combinations of one or more of the associated listed
items.
[0053] Spatially relative terms, such as "beneath", "below",
"lower", "above", "upper", and the like, may be used herein for
ease of description to describe one element or feature's
relationship to another element(s) or feature(s) as illustrated in
the figures. It will be understood that the spatially relative
terms are intended to encompass different orientations of the
device in use or operation in addition to the orientation depicted
in the figures. For example, if the device in the figures is turned
over, elements described as "below" or "beneath" other elements or
features would then be oriented "above" the other elements or
features. Thus, term such as "below" can encompass both an
orientation of above and below. The device may be otherwise
oriented (rotated 90 degrees or at other orientations) and the
spatially relative descriptors used herein are interpreted
accordingly.
[0054] Although the terms first, second, etc. may be used herein to
describe various elements, components, regions, layers and/or
sections, it should be understood that these elements, components,
regions, layers and/or sections should not be limited by these
terms. These terms are used only to distinguish one element,
component, region, layer, or section from another region, layer, or
section. Thus, a first element, component, region, layer, or
section discussed below could be termed a second element,
component, region, layer, or section without departing from the
teachings of the present invention.
[0055] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the present invention. As used herein, the singular forms "a",
"an", and "the" are intended to include the plural forms as well,
unless the context clearly indicates otherwise. It will be further
understood that the terms "includes" and/or "including", when used
in this specification, specify the presence of stated features,
integers, steps, operations, elements, and/or components, but do
not preclude the presence or addition of one or more other
features, integers, steps, operations, elements, components, and/or
groups thereof.
[0056] In describing example embodiments illustrated in the
drawings, specific terminology is employed for the sake of clarity.
However, the disclosure of this patent specification is not
intended to be limited to the specific terminology so selected and
it is to be understood that each specific element includes all
technical equivalents that operate in a similar manner.
[0057] Referencing the drawings, wherein like reference numerals
designate identical or corresponding parts throughout the several
views, example embodiments of the present patent application are
hereafter described.
[0058] FIG. 1 schematically shows an example CT system 1 whose
processor 10 applies an embodiment of an inventive noise rejection
method to CT sectional image displays by executing the programs
Prg.sub.x.
[0059] In the case specifically illustrated here, the CT system 1
has a gantry housing 6 in which an X-ray tube 2 and a multirow
detector 3 are mounted on the gantry (not shown). During operation,
the X-ray tube 2 and the detector 3 rotate around the system axis
9, while the patient 7 is pushed along the system axis 9 through
the scanning region between the X-ray tube 2 and the detector 3
using the moveable patient's couch 8. A spiral scan is thus
performed relative to the patient. Optionally, a plurality of
tube/detector combinations may also be used for scanning. A second
tube/detector combination of this kind is indicated in dashes by
the second X-ray tube 4 and the second multirow detector 5. It
should be noted that a second tube/detector combination can very
easily generate a second statistically independent volume data
record which is statistically independent not only with respect to
the quantum noise.
[0060] Control of the CT system and also image reconstruction,
including image processing with noise rejection, are effected by
the processor 10, which uses an internal memory 11 to hold computer
programs Prg.sub.1-Prg.sub.n which could also be transferred to
mobile storage media. Besides the other usual tasks of a CT
computer, these computer programs also execute an embodiment of the
inventive method for noise rejection during image conditioning.
[0061] The schematic illustration in FIG. 1 shows a variant of an
embodiment of the inventive noise rejection in the dashed box 18.
Accordingly, computer programs are first of all used to reconstruct
volume data records 12 for the patient 7. From these, two
statistically independent volume data records 13.1 and 13.2 are
extracted for the same sectional plane and are then subjected to
respective 3D wavelet transformation 14.1 and 14.2. In step 15,
cross correlation coefficients are then calculated for the
calculated wavelet coefficients. Next, in method step 16, the
ascertained correlation between the wavelet coefficients is taken
as a basis for performing correlation-dependent weighting for the
wavelet coefficients during the reformatting of a new volume data
record. In this context, either only the weighted wavelet
coefficients for one of the two volume data records A and B or a
combination of the weighted wavelet coefficients from both image
data records A and B may be used.
[0062] In this way, a new volume data record 17 from which the
quantum noise has been eliminated is produced which in turn can be
displayed for assessment by the operating personnel on a display on
the processor 10 or else can be transferred to an external
computer, a data storage medium or to a printout for further
assessment by a doctor.
[0063] If the method described in at least one example embodiment
above is intended to take place in real time, the data need to be
subjected to high pass and low pass filtering online during setup
of the tomographic volume data. Since the volume data are
reconstructed in line with the scanning progress along the z axis
or system axis 9, and 3D wavelet transformation also requires the
data situated in the scanning direction, a certain advance needs to
occur between the scan and the wavelet transformation, so that the
3D wavelet transformation takes place with an offset of a few
layers with respect to the scan and the reconstruction. Such a
situation is shown in FIG. 2, which schematically shows the wavelet
breakdown in the z direction with its computation levels 0 to j, in
this case for j=3 by way of example.
[0064] To be able to calculate the wavelet coefficients in a chosen
xy plane at level j, 2.sup.j+(2.sup.j-1) (L.sub.w-2) axial layers
are required.
[0065] This allows the inner 2.sup.j layers to be filtered.
Consequently, an advance of ( 2 j - 1 ) .times. ( L w - 2 ) 2
##EQU7## images is required. When the central 2.sup.j layers have
been filtered, it is necessary to wait for a further 2.sup.j axial
images so as then to filter the inner 2.sup.j layers again. This is
continued iteratively until all the data have been processed.
[0066] In practice, it makes sense to limit the level of the
wavelet transformation at the top by j.sub.max, since the
significant noise components can be found in the high frequncy
bands situated in the low computation levels. At the same time,
this has a positive effect on the speed of processing. The noise
can therefore advantageously be reduced in blocks for 2.sup.jmax
layers, with ( 2 j .times. .times. max - 1 ) .times. ( L w - 2 ) 2
##EQU8## layers of the corresponding, statistically independent
volume data respectively needing to be available as an advance.
After a further 2.sup.jmax respective primary layers, the next
block can be filtered.
[0067] The description below shows a few more variants, which do
not claim to be complete, for obtaining statistically independent
volume data records. One variant for splitting the available
detected data for calculating independent volume data records is
shown schematically in FIG. 3. This shows how a projection P,
comprising a multiplicity of detector data from parallel rays
S.sub.1 to S.sub.j, is split into two complete subprojections P'
and P''.
[0068] In this case, the data which come from rays with uneven
indexes are associated with the projection P' and the data from
rays with even indexes are associated with the complete
subprojection P''. This method, in at least one embodiment, is
carried out for all the projection angles .alpha..sub.1 to
.alpha..sub.n used, so that statistically independent volume data
records A and B can then be reconstructed from the projections and
the sectional images calculated therefrom. The inventive method for
noise rejection 15, in at least one embodiment, is applied to these
volume data records A and B, and a finished reduced-noise volume
data record 17 is retransformed.
[0069] FIG. 4 shows an example of the application of an embodiment
of the inventive method to a voxel-based reconstruction. In this
case, the rays S.sub.1 to S.sub.k are shown which respectively
penetrate a common voxel V and correspond to a 180.degree. half
revolution. In the case of voxel-based reconstruction, the
individual voxel values for an examination object are reconstructed
from a multiplicity of such ray sets in known fashion, and volume
data records are generated.
[0070] Independent volume data records A and B can now be generated
for an embodiment of the inventive method by, as schematically
shown in FIG. 4, virtue of each ray set S for a voxel V, to be more
precise the detector data record produced thereby, being split into
complete subordinate data records which correspond to the ray sets
S' and S''. From the sum of the complete subordinate detector data
records, volume data records A and B are then calculated on a
voxel-by-voxel basis. These statistically independent volume data
records are subjected to the inventive method for noise rejection,
and then a volume data record 17 from which the noise has been
removed is generated.
[0071] The examples shown above can be applied to CT data records
which have been ascertained by a single focus/detector combination.
If at least two focus/detector combinations or a spring focus
having at least two spring focus positions is/are used then the
respective data records ascertained independently of one another
can be processed further in the same way.
[0072] In addition, it should be pointed out that at least one
embodiment of the inventive method can be performed not only on the
processors connected directly to an examination system but can also
be carried out independently on separate units.
[0073] It goes without saying that the features of the invention
which have been cited above can be used not just in the
respectively indicated combination but also in other combinations
or on their own without departing from the scope of the
invention.
[0074] Example embodiments being thus described, it will be obvious
that the same may be varied in many ways. Such variations are not
to be regarded as a departure from the spirit and scope of the
present invention, and all such modifications as would be obvious
to one skilled in the art are intended to be included within the
scope of the following claims.
* * * * *